2.4E: Defining the Sample and Collecting Data
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Defining the sample and collecting data are key parts of all empirical research, both qualitative and quantitative.
Learning Objectives
- Describe different types of research samples
Key Points
- It is important to determine the scope of a research project when developing the question. The choice of method often depends largely on what the researcher intends to investigate. Quantitative and qualitative research projects require different subject selection techniques.
- It is important to determine the scope of a research project when developing the question. While quantitative research requires at least 30 subjects to be considered statistically significant, qualitative research generally takes a more in-depth approach to fewer subjects.
- For both qualitative and quantitative research, sampling can be used. The stages of the sampling process are defining the population of interest, specifying the sampling frame, determining the sampling method and sample size, and sampling and data collecting.
- There are various types of samples, including probability and nonprobability samples. Examples of types of samples include simple random samples, stratified samples, cluster samples, and convenience samples.
- Good data collection involves following the defined sampling process, keeping the data in order, and noting comments and non-responses. Errors and biases can result in the data. Sampling errors and biases are induced by the sample design. Non-sampling errors can also affect results.
Key Terms
- data collection : Data collection is a term used to describe a process of preparing and collecting data.
- sample : A subset of a population selected for measurement, observation or questioning, to provide statistical information about the population.
- bias : The difference between the expectation of the sample estimator and the true population value, which reduces the representativeness of the estimator by systematically distorting it.
Social scientists employ a range of methods in order to analyze a vast breadth of social phenomena. Many empirical forms of sociological research follow the scientific method. Scientific inquiry is generally intended to be as objective as possible in order to reduce the biased interpretations of results. Sampling and data collection are a key component of this process.
It is important to determine the scope of a research project when developing the question. The choice of method often depends largely on what the researcher intends to investigate. For example, a researcher concerned with drawing a statistical generalization across an entire population may administer a survey questionnaire to a representative sample population. By contrast, a researcher who seeks full contextual understanding of the social actions of individuals may choose ethnographic participant observation or open-ended interviews. These two types of studies will yield different types of data. While quantitative research requires at least 30 subjects to be considered statistically significant, qualitative research generally takes a more in-depth approach to fewer subjects.
In both cases, it behooves the researcher to create a concrete list of goals for collecting data. For instance, a researcher might identify what characteristics should be represented in the subjects. Sampling can be used in both quantitative and qualitative research. In statistics and survey methodology, sampling is concerned with the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population. The stages of the sampling process are defining the population of interest, specifying the sampling frame, determining the sampling method and sample size, and sampling and data collecting.
There are various types of samples. A probability sampling is one in which every unit in the population has a chance (greater than zero) of being selected in the sample, and this probability can be accurately determined. Nonprobability sampling is any sampling method where some elements of the population have no chance of selection or where the probability of selection can’t be accurately determined. Examples of types of samples include simple random samples, stratified samples, cluster samples, and convenience samples.
Good data collection involves following the defined sampling process, keeping the data in time order, noting comments and other contextual events, and recording non-responses. Errors and biases can result in the data. Sampling errors and biases, such as selection bias and random sampling error, are induced by the sample design. Non-sampling errors are other errors which can impact the results, caused by problems in data collection, processing, or sample design.